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Takuya Kawabata, Hironori Iwai, Hiromu Seko, Yoshinori Shoji, Kazuo Saito, Shoken Ishii, and Kohei Mizutani

containing water and ice particles. Therefore, it is worthwhile to study the mechanisms of this warm midlatitude heavy rainfall event in detail. Yamada (2012) mapped the airflow field of the Itabashi rainfall using radar reflectivity, but did not report the related environmental fields (e.g., wind field, water vapor, and temperature). Because of the small horizontal scale of the Itabashi rainfall event, a detailed investigation of its mechanism requires a high-resolution simulation that is initialized

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Stefano Migliorini

forward-model uncertainty or with characteristics that make them more sensitive to misspecifications of forecast error uncertainty in observation space (e.g., with multiple gas sensitivities or with Jacobians that have multiple peaks or long tails)—was used at ECMWF ( Collard 2007 ) to determine an optimal set of (currently 373) IASI channels sensitive to atmospheric temperature, water vapor, ozone, and surface conditions in the clear sky for operational monitoring or assimilation. The impact on

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Mark Buehner, Ron McTaggart-Cowan, Alain Beaulne, Cécilien Charette, Louis Garand, Sylvain Heilliette, Ervig Lapalme, Stéphane Laroche, Stephen R. Macpherson, Josée Morneau, and Ayrton Zadra

accurate all-weather measurements of integrated water vapor when combined with measurements of surface pressure and temperature. Assimilation of ZTD data in an analysis system supplies valuable information on water vapor in the lower to midtroposphere at locally high spatial resolution and frequency compared to radiosonde observations. The assimilated GB-GPS data come from the NOAA Global Systems Division GPS network, covering North America and Hawaii. Observations are available every 30 min and

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James A. Cummings and Ole Martin Smedstad

that indicates assimilation of GOES retrievals near the edge of the disk are more likely to increase HYCOM forecast error than assimilation of retrievals in the center of the disk ( Fig. 15 ). The longer atmospheric pathlength of the surface-emitted infrared radiances at high zenith angles is likely adding noise to the data from an increase in total column water vapor and the presence of other atmospheric constituents (i.e., aerosols). This atmospheric variation is not adequately modeled or

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Kazumasa Aonashi, Kozo Okamoto, Tomoko Tashima, Takuji Kubota, and Kosuke Ito

predicted horizontal and vertical momentums as the dynamic variables, and pressure perturbations and potential temperature as the thermodynamic variables. This CRM also employed a bulk microphysical scheme in order to predict, explicitly, the mixing ratios of six hydrometers (water vapor, cloud water, cloud ice, rain, snow, and graupel) and the number concentrations of cloud ice, snow, and graupel ( Ikawa and Saito 1991 ). Similar to AE , we performed 100-member ensemble forecasts of the CRM, each of

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Hailing Zhang and Zhaoxia Pu

EnKF system with version 3.1 of the Advanced Research version of WRF (ARW-WRF; Skamarock et al. 2008 ). DART WRF uses observations to update the WRF state (analysis) variables including wind components, temperature, mixing ratio of water vapor, cloud liquid water, rain, ice and snow, surface pressure, geopotential height, and column mass of dry air. The assimilation of any type of observation can produce increments for all of the analysis variables through the forecast ensemble sample covariance

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María E. Dillon, Yanina García Skabar, Juan Ruiz, Eugenia Kalnay, Estela A. Collini, Pablo Echevarría, Marcos Saucedo, Takemasa Miyoshi, and Masaru Kunii

Typhoon Morakot . Wea. Forecasting , 27 , 424 – 437 , doi: 10.1175/WAF-D-11-00033.1 . Seko, H. , Miyoshi T. , Shoji Y. , and Saito K. , 2011 : Data assimilation experiments of precipitable water vapour using the LETKF system: intense rainfall event over Japan 28 July 2008 . Tellus , 63A , 402 – 414 , doi: 10.1111/j.1600-0870.2010.00508.x . Skamarock, W. , and Coauthors , 2008 : A description of the Advanced Research WRF version 3. NCAR Tech. Note TN–468+STR, 113 pp. , doi: 10.5065/D

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Juanzhen Sun, Hongli Wang, Wenxue Tong, Ying Zhang, Chung-Yi Lin, and Dongmei Xu

section. The vertical transform υ is applied by an empirical orthogonal function (EOF) decomposition of the vertical component of background error covariance. The transform p converts the increment in control variable space to model variable space. The most commonly used control variables in WRFVAR are streamfunction ( ψ ), unbalanced velocity potential ( χ u ), unbalanced surface pressure ( P su ), unbalanced temperature ( T u ), and pseudo–relative humidity (RH s ) (water vapor mixing ratio

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Ting-Chi Wu, Christopher S. Velden, Sharanya J. Majumdar, Hui Liu, and Jeffrey L. Anderson

1. Introduction Atmospheric motion vectors (AMVs) are proxies for the local horizontal wind, and are derived from sequential multispectral satellite images by tracking the motion of targets that include cirrus clouds, gradients in water vapor, and lower-tropospheric cumulus clouds ( Velden et al. 1997 ). AMV data are assimilated routinely into operational global numerical weather prediction (NWP) systems, and have been found to improve forecasts of tropical cyclone (TC) tracks (e.g., Goerss

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Daryl T. Kleist and Kayo Ide

wind, temperature, and specific humidity is shown in Fig. 5 . Generally speaking, the analysis error is smaller in the H-4DENVAR_NMI experiment, especially for upper-tropospheric extratropical winds and temperature, and lower-tropospheric water vapor. It appears that by going from 3D to 4D, the temperature analysis error has actually increased over the southern polar cap in the lower troposphere. Also of note is the increase in analysis error for specific humidity in the midtropospheric tropics

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